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Study On Conductive Model And Logging Interpretation Method Of Tight Sand And Gravel Gas Reservoir

Posted on:2020-06-11Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y F ZhuFull Text:PDF
GTID:1360330575981108Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
As an important unconventional natural gas resource,tight gas has gradually become the main growth point of natural gas production,but the physical properties of tight gas-sand gravel reservoir are poor,the pore structure and the conductivity of rock are complex.It is difficult to calculate reservoir parameters and evaluate productivity,and the degree of research is not deep enough.In view of the difficulty of evaluating tight gas-sand gravel reservoir,this paper starts with the study of rock physics and logging mechanism,studies the logging interpretation method of tight gas-sand gravel reservoir,and forms a set of evaluation techniques suitable for tight gas-sand gravel reservoir.It provides a strong technical guarantee for improving the evaluation accuracy of oil and gas reservoir and stable production of oil field.In this paper,by analyzing a large number of core experiments and basic logging data in the study area,the logging response characteristics of tight gas-sand gravel reservoir are obtained,and the conductive mechanism of tight gas-sand gravel formation is described by electrical experiments.The results show that the relationship between stratigraphic factors and porosity of tight gas-sand gravel reservoir is very poor,and it can not even show the basic Archie formula shape.It is found that the pore structure of tight gas-sand gravel reservoir is quite complex,and the values of a and cementation index m are greatly affected by it.Archie's formula does not apply to the region's direct interpretation.Based on the rock experiments,the factors influencing the conductivity mechanism of the tight gas Shahezi formation rock are studied,and the lithology of the sand andgravel rock,the high resistivity tuff of the reservoir,the low resistivity tuff,the content of the mud and the interaction of thin interlayer are determined.It is the key to the calculation of saturation parameters.A general gas saturation model for tight gas-sand conglomerate reservoirs is established.The model includes rock skeleton,high-resistivity tuff,low-resistivity tuff,muddy,gas and water.The model is characterized by the contribution of the characteristics of each component to the whole rock.The influencing factors of the general gas saturation model of tight gas-sand gravel reservoir are analyzed.The incoherent function solved by the core data combined with the optimization technique is used to obtain the percolation index of rock matrix,low resistance coagulant and muddy.The general gas saturation model of tight gas reservoir is solved by combining the method of dichotomy and Newton's algorithm for the percolation rate of low resistance coagulant and mud.Through practical application,the calculation of saturation is more accurate.Thin interbed correction is considered,that is,high resolution processing of resistivity log,compensated neutron and lithologic density log,natural gamma and acoustic log,and three porosity log data.At the same time,the calculation formulas of shale content,effective porosity and permeability are given based on the actual data.According to the different log response characteristics of reservoir physical properties and electrical properties,two productivity prediction methods are put forward according to the reasonable productivity distribution of the test interval and the combination of pore types,diversity of rock distribution and heterogeneity.The first is to extract the relevant parameters with the help of the microresistivity imaging log data,to realize the quantification of productivity contribution factor,and to regress the productivity prediction model suitable for this block.The second is to obtain a productivity prediction model suitable for this block.The second is when the exploration area has fewer wells adjacent to each other and the geological conditions are similar.The oil testing adjacent wells similar to the reservoir characteristics and lithologic characteristics of this well are found in the region,and the reservoir productivity is predicted by analogy with the gas test results.The porosity,permeability,whole rock analysis,CT scanning,multi-stage triaxialcompression test and synchronous ultrasonic test were carried out through the corresponding rock mechanics experiment scheme.The dynamic shear modulus,volume modulus,Young's modulus and Poisson's ratio are obtained by combining the rock mechanics parameters with the characteristics of rock physics,and under different confining pressures and different axial pressures,the dynamic shear modulus,volume modulus,Young's modulus and Poisson's ratio are obtained.The ultrasonic shear wave velocity and P wave velocity of rock samples are measured simultaneously,so as to measure the dynamic elastic modulus of core.According to the parameters,the compressibility equation is obtained,and the brittle index of rock is obtained by using logging data of tight gas sand conglomerate reservoir.Prediction of compressibility of tight gas-sand-conglomerate reservoirs.The following innovations are summed up:1.The universal gas saturation model of the tight gas and gravel reservoir is established.The model includes rock skeleton,high-resistance tuff,low-resistance tuff,argillaceous,gas and water,and the calculation of saturation is more accurate through practical application.2.According to the implicit function characteristics of the general gas saturation model of tight gas gravel reservoir,a new algorithm is used to solve the saturation equation.3.Based on the micro-resistivity imaging logging data and the conventional logging interpretation data,two kinds of productivity prediction methods for tight gas reservoirs are proposed.4.Based on the experiment,the compressibility equation is obtained.
Keywords/Search Tags:tight gas, sandstone reservoir, core experiment, conductivity mechanism, saturation model, productivity prediction, fracturing parameters
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